Introduction

This document demonstrates how to use the crosstalk package in R Markdown to create linked interactive components. We will generate dummy data for three GP practices, showing a monthly value over a year. A filter control will allow selecting specific practices, and both the line plot and the data table below will update accordingly.

Data Generation

First, we generate some sample data representing monthly values for three GP practices.

# Define GP practices
practices <- c("Practice A", "Practice B", "Practice C")

# Create a sequence of months for one year
months <- seq(as.Date("2023-01-01"), as.Date("2023-12-01"), by = "month")

# Create a data frame combining practices and months
gp_data <- expand.grid(
  practice_name = practices,
  month = months
)

# Add a random value for each practice/month combination
# Simulate some variation and trends
gp_data <- gp_data %>%
  arrange(practice_name, month) %>%
  group_by(practice_name) %>%
  mutate(
    # Add some baseline variation and a slight trend component
    value = round(50 + cumsum(rnorm(n(), mean = 0, sd = 5)) + seq(0, 5, length.out = n()), 1)
  ) %>%
  ungroup() %>%
  # Ensure value is not negative
  mutate(value = pmax(0, value)) %>%
  # Format month for better display
  mutate(month_display = format(month, "%Y-%m"))

# Display the first few rows
# head(gp_data)

Interactive Visualization and Table

Now, we wrap the data in SharedData from the crosstalk package. This allows different widgets and plots to react to selections made on the data.

# Wrap the data frame in SharedData
# Use practice_name as the key for linking
shared_gp_data <- SharedData$new(gp_data, key = ~practice_name, group = "gp_selection")

We can now create the interactive components: a filter for the GP practice, a Plotly line plot, and a DT datatable.

# Create the filter control for GP practice
# Allows selecting one or more practices
filter_select(
  id = "practice_filter",
  label = "Select GP Practice(s):",
  sharedData = shared_gp_data,
  group = ~practice_name # Filter based on the practice_name column
)
# Create the Plotly line plot
# It uses the shared data, so it will update based on the filter
plot_gp <- plot_ly(shared_gp_data, x = ~month, y = ~value, color = ~practice_name,
                   type = 'scatter', mode = 'lines+markers',
                   hoverinfo = 'text',
                   text = ~paste("Practice:", practice_name, "<br>Month:", month_display, "<br>Value:", value)) %>%
  layout(
    title = "Monthly Value per GP Practice",
    xaxis = list(title = "Month"),
    yaxis = list(title = "Value"),
    hovermode = "closest" # Improve hover experience
  )

# Create the DT datatable
# It also uses the shared data and will update
table_gp <- datatable(
  shared_gp_data,
  rownames = FALSE, # Don't show row numbers
  colnames = c("Practice Name", "Month", "Value", "Month Display"), # Nicer column names
  filter = "none", # Disable individual column filters as we have the main filter
  options = list(
    pageLength = 10, # Show 10 rows per page
    autoWidth = TRUE
  )
)

# Display the plot and table
# They will react to the filter selection above
bscols(plot_gp, table_gp, widths = c(6, 6)) # Arrange plot and table side-by-side

Explanation

  1. Data Preparation: We created a tidy data frame (gp_data) with columns for practice name, month, and a simulated value.
  2. Shared Data: The SharedData$new() function wraps our data frame. We specify key = ~practice_name so that crosstalk knows which variable links selections across different components (the filter, plot, and table). The group argument ensures that widgets intended to work together share the same selection scope.
  3. Filter Control: filter_select() creates a dropdown/select input. It’s linked to the shared_gp_data via the sharedData argument and targets the practice_name column via the group argument.
  4. Plotly Plot: plot_ly() is called directly on the shared_gp_data object. Plotly automatically understands how to interact with SharedData objects, filtering the data based on the current selection from the filter_select widget.
  5. DT Table: Similarly, datatable() is called on the shared_gp_data object. It filters its display based on the selection.
  6. Layout: bscols() (from crosstalk) arranges the plot and table side-by-side for a cleaner layout.

When you select one or more practices from the filter dropdown, both the plot and the table will instantly update to show only the data for the selected practices.